{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "tf.logging.set_verbosity(tf.logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据简示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-2-b0ffac263758>:1: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "WARNING:tensorflow:From /home/surveyzhang/anaconda3/envs/vi/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please write your own downloading logic.\n",
      "WARNING:tensorflow:From /home/surveyzhang/anaconda3/envs/vi/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting ./train-images-idx3-ubyte.gz\n",
      "WARNING:tensorflow:From /home/surveyzhang/anaconda3/envs/vi/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting ./train-labels-idx1-ubyte.gz\n",
      "Extracting ./t10k-images-idx3-ubyte.gz\n",
      "Extracting ./t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From /home/surveyzhang/anaconda3/envs/vi/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "(55000, 784)\n",
      "(55000,)\n",
      "(5000, 784)\n",
      "(5000,)\n",
      "(10000, 784)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets(\"./\")\n",
    "\n",
    "print(mnist.train.images.shape)\n",
    "print(mnist.train.labels.shape)\n",
    "\n",
    "print(mnist.validation.images.shape)\n",
    "print(mnist.validation.labels.shape)\n",
    "\n",
    "print(mnist.test.images.shape)\n",
    "print(mnist.test.labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到images里面有数量不等的图片，每张图片是28x28长度的一个一维向量， 所以用的时候需要先给它还原成28x28的二维图片。labels中则是图片对应的数字的值。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZnhOu3cdd1tPiNnsyTvPB7lxviqwokT6JiGzJsLuydKkU+XNOv9lXmbiORF4ClWj+uMIuAQouXXh6xmlOu+ZS/HNHxtGHO8WzXv7O9qmGO5cYCnxWTV0Y3WktKHnbTmnulM+pYuf2d2p3V6xKG7ME8VXtTbvk8JcH7Y6AJ1XKr/WB5eiQiY+adoVTV+F9u+y02Wv2b39pWYZUEO6EAQDwhCQMAIAnZXo4Onutu7Sg+eDSsdRgU+f8B0Hm7dvtlNOfq55vu0RX/1u70XvqrckmvrXjN0670TefbeLac9zhxJRvpuf73MltM5zymm51TJx2tv39+PbwV5x2wWVIobDPphmf3WDj+3/K93Xh3zX3j4tYtyzLvaapX+X/+4OS1/qHq02sZqebuOmwOU47nWOHow/eMT/+HSsh3AkDAOAJSRgAAE9IwgAAeFKm54RLizNmb3fKY2s8GyjZrSmvmXON067mZ1Pj2a3SK7BFZ5eZF5j4m8PfcZr1v+sZE4ck5NTdv+HIfJ+6V/W3nHKnivZxSYHPnOHPF/w82ur9G52ato/bre7KwpKH8qp2cuSTyJ5Ye0bYT7bGtzOIqO3zA51yk0fsNsI6277DYt02uKzhThgAAE9IwgAAeMJwdDG4qNpMp1wlKc3EC7PsIdJVhtcosT6VFTX67jPx/ePcIeaH69p/1yztPu6Bg38zcUhsZfiJR8HlRutz9pj4uU3HO+2+GN7FxC1Hu7utMQRd9u0LJR+4EeLmoWYdTdxQ3GV+OrxxOcOdMAAAnpCEAQDwhOHoGG0YaIcz6ya733JelmW/pXn5w4NNXOczd5gTItkrV5n493MaOnUt/pv/N6BFROZ1HWXik2ZeYuI/N1eL+JgWQ+zAsp46y6mrLVybRDayycdO+cgn/2bi5rf/HN4cKDHcCQMA4AlJGAAAT0jCAAB4wpxwlFTFik75wv72xJ8doX1OXY8pA0zc6EXmGqOVvWq1U25+5eoILUV6ip0vriZLAnFk5X0pRKK75+0rnXLr3k/ZONV9/0rIXcoG+MKdMAAAnpCEAQDwhOHoaIXcwczXxp9i4s9+7+rUNXqXJQ9ASWv8L3fq57Z/HRexbXOWpKGU4E4YAABPSMIAAHhCEgYAwBPmhKOks9xlSE3uYU4JAFA03AkDAOAJSRgAAE+U1uwjBACAD9wJAwDgCUkYAABPSMIAAHhCEgYAwBOScIBSSiuldimlHoqyfR+l1M68x7WId/9QOFzPxMM1TSxcT5Jwfjpore/ZX1BKjVBKLVBKhZRS1wYbaq1Ha63TSryHKIzw63mqUupXpdR2pdRSpVS//XVczzKD92hiCb+eHZVS05VSu/P+t+P+ukS8niThA/tdRAaKyK++O4KiUUqlishYEXlRRKqLyKUi8pRSqoPXjqGoeI8mCKVUBRH5SEReF5GaIjJGRD7K+3lCIgkfgNb6Wa311yKy13dfUGS1RKSaiLymc00VkXki0tZvt1AUvEcTSlfJ3U55iNY6U2s9TESUiJzqtVdxRBJGuaG1Xi8ib4nIdUqpZKXUcSLSWER+8NszAHnaichM7e4i9XvezxMSBzigvHlLREaJyNC88gCt9UqP/QFgpYnItrCfbReRdA99KRHcCaPcUEq1FpF3RKS3iFSQ3E/XdyilzvbaMQD77ZTcKaOg6iKyw0NfSgRJGOXJYSKyQGs9QWsd0lovEJFPROQsz/0CkGuOiLRXSqnAz9rn/TwhkYQPQClVQSlVSXK/HJCqlKqklOLfrWyaISIt8pYpKaVUcxHpKSIzPfcLRcB7NKFMFJEcEblFKVVRKXWLiGgR+cZrr+KIX9QD+0JE9ojI8SIyIi8+yWuPEBOt9RIR6SMiwyR3nmmSiHwguXPEKLt4jyYIrfU+ETlPcqeMtorItSJyXt7PExJJ2JUpItOVUg/s/4HWuqvWWoX9N1FERCl1nVJqa97jQn66jALkdz3f1VofprVO11o30FrfqbUOiXA9ywjeo4klv+s5Q2t9pNa6stb6CK31jP11iXg9OU8YAABPuBMGAMATkjAAAJ6U6GYd3ZMuZuzbky9D76kDtyocrqc/8bieIlxTn3iPJpZoryd3wgAAeEISBgDAE5IwAACekIQBAPCEJAwAgCckYQAAPCEJAwDgCUkYAABPSMIAAHhCEgYAwBOSMAAAnpCEAQDwhCQMAIAnJXqKUlm27O32TvmHLs+b+IreNzt1yd/+WiJ9AhDZkiePNfGtZ37m1H16+XEmDs2cX2J9wgEca//OLrvVPYRo4cljTNxi4rVOXfMrfotrt+KJO2EAADwhCQMA4AnD0VHSK6o65donVjbx5lYVnbqDvi2RLqEYZZ7d2cSb++506mZ0fiOq5+i/6kQT//BZB6eu2YtLTZy9dl0sXcQBpNSv55SHn/uyibtX3uPUjTmmh4lrz4xvv1CwdYOON/HDN71k4tMr73LaZWkbDz36badumLTO97nX33y8U673pp16yNm0udB9jQfuhAEA8IQkDACAJwxHR6nqKhWx7pBL/3DKOS/EuzeIhUqtYOKFT3Vy6j4552kTt0h1pxdCUT7/Cw2+t4/p+51T1/Hw3iZucCHD0fGw5IbGTjl8CBr+qIr2PbXlkiOcuu/+/qSJq6gKUlSr/mGHoKfeOMSpe/fGBiYeNuRCp+6gFyYX+bVjwZ0wAACekIQBAPCEJAwAgCfMCReDPdmpTrnosxqIhwXPdDTxwnOec+qSpJKJQ6IlGv1WdnXKoxpOith2WEe7pOLJ2iebuLQsk0gEDbus8t0FRLD033YeeE7v4WG10f3FfGFrMxO/+NrZTl19+cnEmbXttzhSVbLT7sr0tSbufNdTTt3VcpuJS3J+mDthAAA8IQkDAOAJw9FRqnb22oh12z5wd+o5SP6I0BLxFlyGJOIOQc/pGRwGc4ep1ubsNvFJY//u1DUbu8/EFRfZ5UU5Gzc57Tq9c6WJp3d+3an7dU8TE+t9WRF6j8La2/NoEw9t9kxYbarAn+CypKpttxT68Z/tTnfKH9xxuonrf/JTePNCywj7W/H2P54w8RmdBtl2N0wt8msVhDthAAA8IQkDAOAJSRgAAE+YEy5ATlf7tfrx7Z516n7bZ+cU674x26mLdptDFL+1Nx7llBeeE5wntNds9LZGTrv/9e1u4pY//hzx+bMLeO3MzMhzkONX28PKK+9YVsCzoDD21LbX9PAKzAH7pFLcdLLkP/bv59yjwpcl5S+47G/Dhe6ccMXV0c3NNvnEfoejfeNrnbrpx402cfjypaYpdplitfkl97vEnTAAAJ6QhAEA8ITh6ALkVLSfUdKUe7JOlra7KoV27CixPqFgA/p95JSTxJ5+9cimtiae3CvDaaeW/xbV8ydXq2biVdcf5tTd0f5/Jp6xz52UqHwGQ9A+/Zjp3m+kryxoYgGxyDzNPZls7lXRDUHfuqaLidefbYeBczatiakfyd/+auJG37p1YxccauJL0jbE9PzFjTthAAA8IQkDAOAJw9EFWH4+n1HKmpywz5XBwxg+fbiridOXR/4GtCS535rMObmDiXsO/9rE/Wu4Y13Boe+zF5wX9qSrI78eYta6/5yo2g1Z1d0pV/g8vrsglRfrbznexAMHfBjVY4LDzyIiy06279nQ7vJ3oAlZBgAAT0jCAAB4QhIGAMAT5oQLkH4IS48SSZV1+w7cSNw5YBGRz14fGdXjzl/cw8RJF+526nKiegYU1sC6wXl5FbHdgs9aOuUG8mecepTYkjq0ccqP3mJ3oOpWeXd4cyO4E1ZwGZJIfOeBVad2TrlJ6q8RWooszso0cfWlJbeEjTthAAA8IQkDAOAJw9FIKIv21HV/UH25CV96dZiJH11/mtNs4h8tTPz50cPEVdlE20J7Tdz5k785rVrfbpfLhHbtirbLKAGNP3SHn5keiM2Jr7nDuQUNQQdN/fBwE9ff9FOx9qkgCwZUccpHV9QRWopM2GV31Kv80ZS49Skcd8IAAHhCEgYAwBOGo8MkVbJnSp5QP/Km+yM3nBwo7Yxjj1AY825s6/7gg19MeGiyHVYeWu9Hp1lSPTtEFgoMP4c75ZnBJs54zB1W4xzpkhHcpalVavAaVHLarc4JDJVmMwAdq403HGfiATWfDKu1B9uszdnj1Nz2h901rtH/1ps43lcipWljE0868+mw2sjv7R82twiUNhZvpwrAnTAAAJ6QhAEA8IQkDACAJ8wJh0mqUd3Ez9T7LGK7ST/YA92bSwEn8iDuMs/ubOKVl7k73SQVsItSULIKfB7V7uxutzkXmLjeYyW3vAK5kuse7JQ7XTHLxNWSKoU3N7qO/buJWy7iPRqrHXaKVdKSKkZs98SGU9zHnRicVy25OdYFNx5q4uD3QMJtCSw3FBFZN7S5iasyJwwAQOIjCQMA4AnD0WGym9Q9cCMRafR5Vpx7gqCk9q2d8iEjVpt4VMMXTRwSd0ecSMuG7lrX2Sn/b8pRJn6++xinbnSr103c+xI7xJn2LkOcJaJOTac4quHn+TbbHja8mL6Me4yS9PlXRznlpjK55F5c2WknnRzdQ/6+6iynXPX9XyK0jC9+SwEA8IQkDACAJyRhAAA8YU44zMZ79ub78x7zeznlChN/N3HkczlQFBv72e3yJtz7hFNX3VmaEnkZ0u1rjzXxZ9/YOauMp90tSTPW2lNTnjjlSqfus9dHmviy++yytY/fdecqER85VStE1W5WlntiziFDWE5Wkg790d/WoNuuPMbE8y95NqrH/PSju8Wtr6Wm3AkDAOAJSRgAAE8Yjg7z/GFvBEr2u+5rtldz2tXLXlVCPSo/dlx2rFMODkFXD9sZaV6WXSL29LruJl4wpJ3TrvqHv5m42V67ZMLdV8uVPOl3p9z63RtN/PvFQ0w89vSbnHapX0wr4FkRq/Qn10bVbsAMdxqhgcyJR3cQQeO75zvl9eOL9/lTGtQ38aIbGzl1v1wVPN0p8q5eb+2wS1AzXt7i1PkaTOdOGAAAT0jCAAB4Uu6Ho1OauMMa6cp+ozJZpZZ0d8q1je3dbzkHh6DH7qrl1L18ydkmDv0218TpYd9wjLRjVkGSKrtD3+2OWG7iioHfiVBKdIdDoPBSGjYwcUbaiojtrlx+mokbX7/GqfP3Xd3y6YQai53yhy3t9FLOoqVRPUdym5YmXnRNHaduyEUvm/j0yrvCHhl5CDpozI3nmjhlzvSoHhNv3AkDAOAJSRgAAE9IwgAAeFLu54T3jnLLGal2PjAncLh72rvuEiXEX1JgJ6w7v73Eqcv4bWqxvlZyndomrjLWnet9p9mngRLzwCVhXY+GJh538DinLlnZe4cte+0uWUn73CUnKtXutKWz9hV3F8uNlqPsErH7e3R06u47yC4BvK7aSqcueZz9+zlrdwOJRseqk0x8ZXp0S9PCjdtld7L7+1eXOXWtf7bL1mL5vkg8cCcMAIAnJGEAADwpl8PRyRnNTXx7k3ER212+zO7EVO1tPwc+lyd1ZrpHYWwJ7THx1B5DnLrOLw4ycZt//WHinPUbIj5/Sv16Jt7Vob5TN2joWyY+u8o2py44bPXsVvu7U/n7+RHbIX6C00Sftg68fxe67Vq+P9DGt/rZnD8RZC9dbuIJw05w6gbdb/9dw3e1611ttS0E42KwW7vTC89utsPk3/1fZxNnTJvitCuN71HuhAEA8IQkDACAJyRhAAA8KZdzwvvqVzdxt8qZEdstfKeVietqDgiPt/S33Xm7k1oMNvHvA55x6hb2fMHEc063ZyINWnRpxOd/o409ISt8/iq4HCp83uj2tXb7vfk324PA1Y7fBfFRabO9Ckuy9zh1zVMq5/uYPWHzhFXWco9R3Gq9NNkp/2tANxP3P2iiU9cmtXi3/Q1+H+O1oWc5dXVGBPs1u1hfN974LQUAwBOSMAAAnpTL4eiC9F91oonrvbXAxJzIUvJqzbf/6i9sbebUta20ysRdK9mh5C/bfVDAM1aKWPPCtsYmfvqTnk5dy3tnmFjtZQi6JKS9Z5cEXnLIYKfut388Z+IHN7Y28QcUHIZUAAAgAElEQVQjTnXa1R/OFFK8Lem818R3tbjcrbv2EBOfceY0Ez95qDvt1O7Vm0ysCvhD2/zNTSauM3dy5IZlDHfCAAB4QhIGAMATpbU+cKti0j3p4pJ7MTi+DL1X7CcP+LyeKU0amXjRozUitnvkiA9N/NOOFiYeP+EYp13Tu8vW8FY8rqcI71GfEu09Wt5Fez25EwYAwBOSMAAAnpCEAQDwhCVKKJOyl68wcdPLVkRsN0KCS5vsLkxNpWzNAQNITNwJAwDgCUkYAABPSMIAAHhCEgYAwBOSMAAAnpCEAQDwhCQMAIAnJGEAADwhCQMA4EmJnqIEAAAs7oQBAPCEJAwAgCckYQAAPCEJByiltFJql1LqoSjb91FK7cx7XIt49w+Fw/VMPFzTxML1JAnnp4PW+p79BaXUOUqp2XkX/ielVNv9dVrr0VrrND/dRJTCr+epSqlflVLblVJLlVL99tdxPcuM8GvaUSk1XSm1O+9/O+6v45qWCeZ6KqXqKKV+VEptUkptU0pNVkp12d8wEa8nSbgASqmWIvKGiPQXkRoiMl5EximlOIe5DFJKpYrIWBF5UUSqi8ilIvKUUqqD144hZkqpCiLykYi8LiI1RWSMiHyU93OUPTtF5HoRqSu5f3P/KyLjE/lvLkm4YGeIyA9a6x+01tmS+wtRX0RO9tstxKiWiFQTkdd0rqkiMk9E2hb8MJRiXUUkRUSGaK0ztdbDRESJyKlee4WYaK33aq3n5f29VSKSI7kfrmr57Vn8kIQLR+X9d5jvjqDwtNbrReQtEblOKZWslDpORBqLyA9+e4YiaCciM7W74cHveT9HGaWUmikie0VknIiM0lpv8NyluCEJF+wrETlZKdU1b3jrbhGpICJV/HYLRfCWiPxLRDJF5HsRuUdrvdJvl1AEaSKyLexn20Uk3UNfUEy01u0ld9TqCknwD8kk4QJoreeLyDUiMlxE1opIHRGZKyKrfPYLsVFKtRaRd0Skt+R+mGonIncopc722jEUxU7J/WMdVF1EdnjoC4pR3tD0WyJyVyJ/b4MkfABa6/e11odprWuLyH0i0kREpvrtFWJ0mIgs0FpP0FqHtNYLROQTETnLc78Quzki0l4ppQI/a5/3cySGVBFp5rsT8UISPgCl1JF584cHicgIERmXd4eMsmeGiLTIW6aklFLNRaSniMz03C/EbqLkfnnnFqVURaXULSKiReQbr71CTJRSxyqlTlBKVVBKVVZK3Sm535T+xXff4oUkfGBDRWSriCwQkS0i0tdvdxArrfUSEekjIsMkd95wkoh8ICKjfPYLsdNa7xOR8yR3imGriFwrIufl/RxlT0UReVZENonIahHpISJna63XeO1VHHGKUoBSaq/kfmFnmNb63ijaXyciT4tIJRFpq7VeGucuohC4nomHa5pYuJ4kYQAAvGE4GgAAT0jCAAB4QhIGAMCTEt0Uu3vSxUxAe/Jl6D114FaFw/X0Jx7XU4Rr6hPv0cQS7fXkThgAAE9IwgAAeEISBgDAE5IwAACekIQBAPCEJAwAgCckYQAAPCEJAwDgCUkYAABPSMIAAHhCEgYAwBOSMAAAnpToAQ5ASUtp3NDEW4+pb+K1Pfc57QYcMcnEg2oudOoO++E6E4eWVzVxi/t/d9qFdu+O3I9DDzFx9tp1B+o2kFCyux1p4k3tKjp1ew62Z0zoFrtMfGeHL5x2farb983nu93nGDyij4nrPfZT0TpbwrgTBgDAE5IwAACeMByNhLJm8PFO+Z7r3zLx+WkbIj4uKfB5NCQhp27mCaNt4QQbdth7q9Ou8X2Rh8EqvpNj4uyTIjbDfsoexbphwHFO1YCbPzRxv+prYnr6EdvqmfjDXseaOLR8ldNOZ7nTFojetqvsv+s3jw4zcUXlpp2Q5H/kcZK4x/FmaduuW2V36ueHW5408fHJt5u4wSOlf2iaO2EAADwhCQMA4AnD0WGSOrQx8YLbKpv46o6/OO1urjXFxN2eHOzUHTKk9A+BJJLkthkmDg4/i0Qegv4zJ9Mp/5FdxcQ5kurUHVXBDkkmB4ZJf79+qNOu83Y7PH3ok+7vwAm1lph4glTLt0/lXlKyCVfec4yJZ/UfHvEhmdoO86/Jdq9ppcBo5sHJVZy6PtXssHOfie+beOiWFk67r3seZuLs5Ssi9gN/tf28nSZOVfbahg8/r8jeY+J7VvWK+Hy/zG9mn6+qO03wQ5fnTXz8eXbVwsqn3G9R60z3d6Q04E4YAABPSMIAAHhCEgYAwJNyOSesKtp5gnX9jnTqfrnLzvPtCNl5h2Pf/rvT7ruOdu7o5KumOnULhhRLNxGl+XelmTh8Djh4DU+Z1tfEdYdWctolT/w14vNvvMEukek58DsT313nN6ddjjv95Phhc/NA6c/IDcux1YOjnQfONnGHN+08fLM7Jjvtktu0NPH8f6Q7dbNPfcHEwSUzt9Zc7L7Yxzb8qmtTpypn46aIfYRIk76rTTzwc7sub/bmQ5x2NQMr/XIWLpFIMmRzxLpjXvibiReeY+eHO95+s9OuwcOl7/s63AkDAOAJSRgAAE/KzXB0UiU7/Dh/SHsTLz7HHfZ6Zqsdwnrv/jNN3PzdsKGuDDu8OLN5R6dOn2PXRqTstksoUr6eXthuIwr/O/H5QMn9XDnwD7vkod75c2N6/jov2mv/zQa7Zdbdw3/Lr3m+Fnxuf68aMBwtIiIqxf3zU6FLdMO7h/3PDjG2DBuCDsqZt8i26+3WndjPjoE+ducIE3etlOW0Cw5Pf51+uPskDEcXKGfLFhPPGGmndGoscZcJ5SyMPBUUreRd+d9PtuuxwClve7jIL1XsuBMGAMATkjAAAJ6QhAEA8CRh54STqrjb1K1+s7GJF3e2yxOe2tLSaTfh5pNNnPbtzxGfP/hV+ipbtjt1gyZPNPGodfar+du+PkCnEZPDK9htJsO3xJu60C4ryZCiz+Glz7bzuT/sdZc51Z6THd7c0CpiVbmV3KiBU5565Fv5tntmazOn3PoFO9eYE944SnVG2LnksX2PMnHXepHnmBG72qP8/Lv2rPO7U35DGkRo6Q93wgAAeEISBgDAk4Qajg4OQc9/8jCnLjgE/cTmVib+rldbp13yssJ/XX7lte6QdrfKE0y8+SD7fK/WaO+0y9m6rdCvhb86ZfaFJv7ysHedujFdR5n4IXGXkkUru5vdVe2gB+w0RLMU9/rVuX2ZiXd95D6Hyv/c8nJt+aX1Itbt1HYZy9sPn+nUVZ8beZooFkuvbWLiH8e7p6V1qRgy8aJ+bn+b3Wt3hNLZkaciUPwyz+rslK/tPjHfdh9u6BT2k9K3PJA7YQAAPCEJAwDgSUINR/95ZQcTL+71rFP3yW67yf9357Yzcfay5UV+3X3VI481zttrh7AYfo6PtEH21/j5992pgX7VF5p44XNHm7jtf9c67dafbr81ec5Nk5y63jXsoR71UoKnNLgnNrzabLyJe/ZwN47Prsx4tIhIcu1aJr7zmncjtnt/h/1We/U3inf4OVzOHLur0jUT+jl1i3vZaax5vd2/KWd/ENiGa9rs+HSuHEuuVs0pr7/c/t2+YZA739On2ioTL8/eY+JNj7uHblRiOBoAAOxHEgYAwBOSMAAAnpTpOeGU+u6SgTsGv2ni1Tm7nbpH7hto4mpLiz7HlNKsiYl7nvVL5IaIu+BpOa8NPcupG3CfrZt/bmBO71z3OZICn0dDEnIrw+Z+97tz3XFOefx3duel1rNWOXU3PGZPcJpwrzvXVZ6owGlmV6Zv8NiT/FWbH/YnsVf+7UREFvS3/18yro9ThxJQUkd3WeiarjVMvL2VXerVt4v73YzBtb8t4FntlnSnfXqbiTPGT4mxlyWHO2EAADwhCQMA4EmZHo4O1XaH9S6sajd2/8/GY5y6am8Wfgg6eOj46kFHO3V39X3HxJellb6vvZcne8611+bEG6YW+/P3+aO7if+8rZGJk2Yudtq12G1/x9g/qWi+3dI6UNrqrR+IXsqhhzjlaybZQxvOqLLOxKniDhGnquQiv/YJf7fTjRnvFP/fgHjiThgAAE9IwgAAeFKmh6ML0qvaDKf8cb9bTZy6O/LuRZvPtrutfHz8cyZunuIOoXy4y36jr8W4/k5dcJedqZsbB2rWFNxpRG3zdfabyZfc/oWJB9VcGNYyus+ZwSGxts+6u101fOinQMkOjYZ/h7ogSaowrRPX0uubRNVu9tv2G7R15acCWqK00DXd6cHzq24OlCrE9bWdA1JCsZ4y7Qd3wgAAeEISBgDAE5IwAACelOk54dCsBU454137NfWFlzzn1E25zz0BJRqf76lt4vNG/Z9T1+ix6SZu3Wq7+8DALjuLpto54WbMCccspXFDp3zv3WNMfFaVHSYO3+1qc449HL7XTHsNXz3sFaddi1S7K1bK3iJ1NV8hzeddEZG9jff57gLiZa27VPOY6VeYuNPBq038/TeHO+0qr1eSnz113e/u/OfCt018YdpGp67H3RNN/Kl0NXH62/E9gas48JcBAABPSMIAAHhSpoejRbvDFS3+Zocejp5/o1MX6rFF8rN1Q7pTbvKBjSt8bndeaRi2TCL4ynrmfKfuwY2HmfiqM+wm5D/dEd+v6Sea5FYtTPzIhNedulapdknRimw75Nzj9cFOuxbP/WHiWqvt8qWer7m/H/NPHWXbnRE2bfB0YEefGJc/jH7zTBM3YMkNElDOFvdv7EG9bDl4nElTmSyxeO0ZuwviMy9Xceq+OdzuYDipb0tb8W7YblylcPkSd8IAAHhCEgYAwBOSMAAAnpTtOeEC1HkxbN7hxfzbHVwMr5Vcu5ZT7lTFzk1P3920GF6hfFp0X5qJg3PAIiJf7bFz+f9+6BYTN3nZve6RTjNqcbW7remFk8428YR27zl1xw60W54ePDy2+dwGDzMPfCBrc3abuNqK0n8OVdXFfMejJGWvtScxpZ3p1t0+9QQTf9r6QxMf2/cmp91f8kIpwJ0wAACekIQBAPAkYYejS5Ku7w5qn11lp4lv/d6e9pMh00qsT4nglWNfilj3+K1Xm7jWJ0UfYlryeTNbcEew5PqB4008bnhtQXykJ9kph8xqNq4c59dNbmOXtFzVd0LUj2s8ZqmJS//gefFIrlnTKet9dge00K5dJd0d4/PvOpn46cvs1M/5N37rtPv+xUol1qdocScMAIAnJGEAADxhOLoYrO5eK2JdysbUEuxJYkkO7EuWFPZ5seKmzPDmRdLkFTu0+Hpv97CILpUXm/iTOhkmztm4qVj7UB6kzwl8o/gMty5N2UM0jrvV7lY379X49qn+K3aHtNtqLorYrs0Yd5e1Zn9OjdAysaQ0bGDith+tduo+/shOtzW6P74rAFRF+/uxYvCRTt0dPT4Mb57bpwobw37SIN92PnEnDACAJyRhAAA8IQkDAOAJc8LFILOmPnAjFNrrm443cad6Pzh1y/9m42aPtDVx6Le5Mb2Wzranq2zLcU9oaVPBflbdcL6dE649MvqlUTsuO9bEZeGg8Xhp+PZyW7gtcrvDq9hzd+bJIcXej6WP2rnMd+s/Faip6LQbuc1+P6DF04udupzs8rEwadvR9U38aN1xTt3d1/9o4iPr/M2pazVqe6Ffa+nFNUycVTPk1D1w2vsmviTNnX9OEmXi4KOee+Aip111KX3vPe6EAQDwhCQMAIAnDEej1PriqyNsobc7HD3zhNEmXvORXa705IZuTrvPvu8k0Rh7wRAThx8WMSPTflY96I3fTewOlhXson9+YeIJb1crxCMTiw7sqjR0Swun7taadrj38vQVJn7o1R5Ou1ZP2IMeQjPnR/W6Oy8+xinPuOppE1cOLI0KDj+LiIy70E6J5PwZeflSIqu6eo+JH9x4mFP3zzqzTbzgguecuqQLgkPEweWGymkXrHMeH2U7EZENgcM/unx0u4kz3ncPaimNE4fcCQMA4AlJGAAAT0jCAAB4wpxwHCQr+9mm5hyPHSnjWgxZYuJfLnW3/zymYpaJG6TYc3aeDFvK9OSlbjmSJLHPHwqb7f1sR3tbt3u3xGLkvC4mbiSzYnqORJCzdZuJv+7pzi/KxzYMzg8v6jbKafba0XbJ0n/fdpegBF15wTc2rv6kU1dZVQlvLiIiz7x+rlNuMC++WzGWCT/PNOF3tx3nVJ3+Dzuv/7/W7zh1wW1Iw+d3g9zlRXbW9o0d7ul0F6XZ7UXbfT7QqWs81j5Hy09+MXFpnAMOx50wAACekIQBAPCE4eg4yNF2OLPmvJ0ee1K25azfYOJHz7zQqVsw8CAT9+v2tYkH1Yptx6w+K04x8dQJ7jBps9ErAqVVEotGF5ffIehIspevcMpvDg0cq3RrIKzp7lR1dfo6G/cdHuWrucPPr2yvZ+IPLjrZxA3m/SKILOXr6e4P7FtPep1zq1O15vJ9Jp5yol2+dNGCy5x2Gz+2JxupwExQvTfc5WdjOtipgoxvpkXd59KOO2EAADwhCQMA4AnD0XEQ/HY0ikfOwiVOucUgW/5GqgbizjG+gt1svpG434gtH9v0+xc8EOOLV+qY+KsmHZ1282+y35o94Wg7/fDDlLYSSesRW5xyaOEyE+usBYXvLP6i0vgpTrnZeBtfJnbnsRRxpyEOCSvvlxNWTvlmc5H6V1qRLQAA8IQkDACAJyRhAAA8YU44DpZk2WVJyVvtDkvhcxwA8qez7PKWnEVLnbqWt9ry+uDPCziwnfceSivuhAEA8IQkDACAJwxHF4Mm/5zslAf+84RAyV1aAwDAftwJAwDgCUkYAABPSMIAAHhCEgYAwBOSMAAAnpCEAQDwRGmtffcBAIByiTthAAA8IQkDAOAJSRgAAE9IwgAAeEISDlBKaaXULqXUQ1G276OU2pn3uBbx7h8Kh+uZeLimiYXrSRLOTwet9T37C0qpEUqpBUqpkFLq2mBDrfVorXVaifcQhWGup1IqQyn1kVLqT6XUZqXUBKVUq/0NuZ5lRvCanpj3Rzn4n1ZKXSjCNS0jwv/mdlRKTVdK7c7734776xLxepKED+x3ERkoIr/67giKrIaIjBORViJSV0SmiMhHXnuEItFaf6+1Ttv/n4j0FJGdIvK5564hBkqpCpL7nnxdRGqKyBgR+Sjv5wmJJHwAWutntdZfi8he331B0Witp+R9kt6stc4SkadFpJVSqrbvvqHYXCMi72utd/nuCGLSVXKP2B2itc7UWg8TESUip3rtVRyRhFGenSQi67TWm3x3BEWnlKoqIhdJ7t0TyqZ2IjJTu7tI/Z7384REEka5pJRqICLPishtvvuCYnOBiGwUkUm+O4KYpYnItrCfbReRdA99KREkYZQ7SqmDROQLEXlOa/2W7/6g2FwjIq9q9uIty3aKSLWwn1UXkR0e+lIiSMIoV5RSNSU3AY/TWke1LAKln1KqoeTOJ77quSsomjki0l4ppQI/a5/384REEj4ApVQFpVQlyf1yQKpSqpJSin+3MkgpVU1EJojIj1rru3z3B8XqahH5SWu9xHdHUCQTRSRHRG5RSlVUSt0iIlpEvvHaqzgimRzYFyKyR0SOF5ERefFJXnuEWJ0vIp1F5LqwdaWNfHcMRdZb+EJWmae13ici50nu9dwqIteKyHl5P09IJGFXpohMV0o9sP8HWuuuWmsV9t9EERGl1HVKqa15jwv56TIK4FxPrfWYvOtXNbi2VGu9QoTrWUb85T0qIqK1bq21Hh3emGta6uX3N3eG1vpIrXVlrfURWusZ++sS8XpynjAAAJ5wJwwAgCckYQAAPEkpyRfrnnQxY9+efBl6Tx24VeFwPf2Jx/UU4Zr6xHs0sUR7PbkTBgDAE5IwAACekIQBAPCEJAwAgCckYQAAPCEJAwDgCUkYAABPSMIAAHhCEgYAwBOSMAAAnpCEAQDwhCQMAIAnJGEAADwp0VOUyppFY44w8YLTRjp1p9400MRVxv5SYn0CgPIguV0rp7z8gtomPqrHbKfu1cbfmThL50T1/N1uHOCUK384pbBdLBbcCQMA4AlJGAAATxiOLoi2ZzKHJORUre5m45ZjS6pD2C+laWMTrzy/vol3ZGQ77VplrDbx+FbjTJzxcX+nXYMJ9vNotRnrnDq9c7eJc/7808QqxX37rLnlaBNnV3b72+iJ6fb5MjMFwF9tv+JYE59910SnbmztWREfl6Xt+zf8b3Ukzw8Z6pQHL+ht4px5i6J6juLAnTAAAJ6QhAEA8ITh6Bg1b7PGxKpiRaeO4cbit27Q8U552uBnTBzt8FOw1cKeL7h1PSM/xzs7DjXxS38738RrTnTfPrOucYe3gs6Z2NfE6sffDtRVIGElVarklJf8u5OJ51w93MTRvq9jlZFawSnPu7Wmresf3jp+uBMGAMATkjAAAJ6QhAEA8IQ54Rh92vpDE5+b1t2py2FOuFgkt2hq4jG3Ph1WW/hf3bE7DzbxhWkbo37cpelrbTzqORMnhX2GDc5gzch065K37c23XXmz/hY7t7/9qL0FtIyv1Ip2KdvsE16O2K5n/SNLojuJT9nlnsE5YBGRWVcPC5SKfl/Y9t2bI9bNveSZiHWPnPKeiV8+uqetmBJ5aVRx4E4YAABPSMIAAHjCcDRKrTU97NKgNhUif148ddalJq76QLWI7VLXbjXx6Ho1nLrM2na5wsDH3nPqzk/bcODOisjsfdrEg28f6NRVmc0hHyIiu461u4/NO3lkxHbBof5Yl6pE+xzBmte3N4zptfBXoRPtsPPSfvbnc08dlk/rv3p/5yFO+Z8/2OWBDce5fw8qf2QPX2ghP5tYdWrnPuklkV8v+D4f1qyqidPjfK4Dd8IAAHhCEgYAwBOSMAAAnjAnjFLrhKunR6xbm7PHxOtn1TVx8lmRn6/uNDvvu/6oZPe1TrPLEKKdAw738faOJq4yljng/LQcuMzEF6Sf79Qtu7aRiTNr2plapSUmoTr7TDzvtBcjtmv9qZ2/b3PH4rDaLbG9eHkUWIYkEj4PPCKqpzhnQS8Th+49yKnL+HFa7H0rxbgTBgDAE5IwAACeMByNUuuTaR1M/Ng53zt1jVLSTDzviuESletsmKrc4egsnRMouZ9NNwaGvk987+8mnnjxE067u+vYIe2ul9zo1KW9+7NAJGfrNlsIxiLS8IFVxfpaOy+xB8TLaW7d4iy7Y1abxzfb/m1h+Lkwgicihe+EFe1SpF8yU02sT11tYiWr82uecLgTBgDAE5IwAACeMByNUitjgN2q5ojafZy6WV1eMXEsOyplhX3jdtwue6D30GXdnLqkoXVM3PxTO6x8YtXbnHbzz3nWxGu65zh1Ge8WuosoorU990Wsu3+V3aA/Z+GSkuhOQtJtmpvYPYghsjZf3+CUm4+w798k+a14OlaGcCcMAIAnJGEAADwhCQMA4AlzwigTmt3vzu91bXdjhJaxqTFtnYkrL10WVhtePrDDM1Y65cxYOoUiWdRtlIlDYfcb06e0NHEL2VRifUo0q7tVN3FSAfd0Y3fVMnHL4Vlu5ZRZUlKCffzrMkUba3fzr7jiThgAAE9IwgAAeMJwNMqEnDkLnHLanOJ9/uwDN/mLVhmRd/SZtdA9HD5D1kVoiXgJiQ7E7jK2WA+FKO9SGjZwymdeMdnEBS0VvPObS02cMWVKxHbFbdW9bjnYx/Blitcst9uq1fxkrondxYbFjzthAAA8IQkDAOAJw9EFCYxZhX/zL/ybdSgfsk470sQTWrlnpE4ObETf6rndTh2jn/G359yjw34S+TzqnFr2G7pL37TnQB/ZeIXTbtChX9rHiPuV2b4v3WTihg/+VJiullmbT3SHox+sOzZi2+6zLzFxmzvmmzjew7vL32lv4pc6vhL145a80NrENbZPLqBl8eJOGAAAT0jCAAB4QhIGAMAT5oQLEtg2Jfzr98Gvt897uLlTl3HDZkHiSEpPN/HDI+w8cPj3Ar7baeeU9IxiXkOVgJLrHuyUdxzf1MR7atn7g6QLNkb1fGPaDQn7ScWIbeef/kJUz9nnj+4mnv55W6euyVP2xJ/Cn+NVNm3qtfvAjfKsXFXbxBnbC7/rXKzuaP+FiY+qGHkGus+KU5xy7c8Xmzje89ZB3AkDAOAJSRgAAE8Yji4OFcrLYFT5kFy7llPe+abdpL5Txcg77rw06WQTt5Rf4tO5Mi7r9KNMnH7vcqdubLPhJg4uCSxoJyZX6oGb5AkOM/95W6PIDX+eacJG4i5DKo/v+rs7fu6UCzq0IaPPtHh3x9j+mZ0S7F0tuDQtcv/mvtTOKdf+s+SWJQVxJwwAgCckYQAAPCEJAwDgCXPCQJiVfVo75WmHDc233YMb2zvlNk+vN3EspzKVB3+cZf/kTGg2wal7Y0d9E2/NqWLij9Z0cNpt+La+5GdYnxedcrfKdqFJ518vd+pq9VwYKG0tuNMwcrR73xb9fH3RJdew381Y/EJjp25O+5ej6lPbd282cYuRfuaAw3EnDACAJyRhAAA8YTga5VJwFywRkfVv1DPxBx0eD2tdwUTDttih6gmPnei0qr705+LrYIKqMc/uQpfxaX+nrs1gO0Scs3WbiSvIH067BmHl/X6/wh2iPKnSopj7Cf+CJ5aJiNR9wF7PsY1Gh7XO/37yqz3u+7zVSLubYUnuilUQ7oQBAPCEJAwAgCcMR6PUWjfoeBNXOM3dxP/Jtu+aOKSj+yz50PKzTXx/0w+duuBOWMHh53DfXmp3fKo+h+HnwqozYnIgduviOTyY+nqtAzdCsdp0/XEmrj0qum8iL3zZDkE3rr/JqRvZ6OtC9+Hmz65xyi3nlr6d7LgTBgDAE5IwAACekIQBAPCEOWGUGjsuPdYpTxv8TMS2qSrZxFk6K6rn/7S1nQcOPj73OWy8LbTXqev2xGATHzLHPUkHfiXXPdjE9VLzX7okIpKytzyeeVT8Hpt5ulPufcLLEVqKtO0zx8TTDrXf7+h32adOuxtrLDSlzFwAAANJSURBVDFxqvrNxFk6/FsCke8Zg+/njDE3mbjlP0rHrlgF4U4YAABPSMIAAHjCcHQYlWL/SSpW3eexJ+XP2u7usQcFbcQeHD6OZRP54OPDn+Nf67o5dfW/+NPEpWWXHeTacXxTE5+f9klYLfcYxa3BiFSnPLmzHQY+pqI7LeQsKeofeXlR8N0b7fs6uHOdiMjI8XaYvNm/fzVx2Nu8VOK3FAAAT0jCAAB4QhIGAMAT5oTDJDVtZOLfjn8pYrvgMpZ6n/LPGKvk2nY7wcuPnOKxJ9bT9b53yt+OTzPxMyd0NXH2uvUl1SVEISnsniL4Hk3ZyWx+cUj5erpT/vv9A0z8/cPDivW1VmVnOuXH1nc38cprGzp1TefapUhlYR44iDthAAA8IQkDAOAJ46jhNm814eGv3mLio06a7zRb9XhLE6d9WPpO5igrstrag9jvO3hCsT//mXMvMvH6SfVthXLb3XWlPZXp0vS1Tt0plXea+JmKkU9Ygl/hS1pe3Xa4iVO/mh7eHMWgzud2t6tODW916mYMGFqk5z5/6B1O+dCngrvVLSzSc5cm3AkDAOAJSRgAAE8Yjg6Ts2mziZsGNv/eFNauspSOb/KWdalr7fD/CTOudOp+6PRGxMetzdlj4u6v2QMWWoxY5bSruMYOLTfMirzB/9svdDLxO1WOc+q2H1nPxOnbE2cYLNGNnNfFxI1klseeJK6c9RtM3PDBDU5drwc7F+m5D5XycVgKd8IAAHhCEgYAwBOSMAAAnjAnDK9yFi8zca2ebl0viW5OqYnYufvsAtoV2I8//4xYV+WPlbZdjM+P+Fh9WuS6tE/TIlcCpQR3wgAAeEISBgDAE4ajAZRZSXvt1mdPbjrMqav18uTw5kCpw50wAACekIQBAPCEJAwAgCfMCQMos5rf/rOJJ0lljz0BYsOdMAAAnpCEAQDwRGmtffcBAIByiTthAAA8IQkDAOAJSRgAAE9IwgAAeEISBgDAE5IwAACekIQBAPCEJAwAgCckYQAAPCEJAwDgCUkYAABPSMIAAHhCEgYAwBOSMAAAnpCEAQDwhCQMAIAnJGEAADwhCQMA4AlJGAAAT0jCAAB4QhIGAMATkjAAAJ6QhAEA8OT/AarNvdID4hLFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5bb194668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "for idx in range(16):\n",
    "    plt.subplot(4,4, idx+1)\n",
    "    plt.axis('off')\n",
    "    plt.title('[{}]'.format(mnist.train.labels[idx]))\n",
    "    plt.imshow(mnist.train.images[idx].reshape((28,28)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "接下来，定义用于训练的网络，首先定义网络的输入。\n",
    "\n",
    "这里我们直接使用上面的数据作为输入，所以定义两个placeholder分别用于图像和lable数据，另外，定义一个float类型的变量用于设置学习率。\n",
    "\n",
    "为了让网络更高效的运行，多个数据会被组织成一个batch送入网络，两个placeholder的第一个维度就是batchsize，因为我们这里还没有确定batchsize，所以第一个维度留空。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tf.placeholder(\"float\", [None, 784])\n",
    "y = tf.placeholder(\"int64\", [None])\n",
    "learning_rate = tf.placeholder(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize(shape, stddev=0.1):\n",
    "  return tf.truncated_normal(shape, stddev=0.1)\n",
    "\n",
    "L1_units_count = 100\n",
    "\n",
    "W_1 = tf.Variable(initialize([784, L1_units_count]))\n",
    "b_1 = tf.Variable(initialize([L1_units_count]))\n",
    "logits_1 = tf.matmul(x, W_1) + b_1\n",
    "output_1 = tf.nn.relu(logits_1)\n",
    "\n",
    "L2_units_count = 10 \n",
    "W_2 = tf.Variable(initialize([L1_units_count, L2_units_count]))\n",
    "b_2 = tf.Variable(initialize([L2_units_count]))\n",
    "logits_2 = tf.matmul(output_1, W_2) + b_2  \n",
    "\n",
    "logits = logits_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "接下来定义loss和用于优化网络的优化器。loss计算使用了sparse_softmax_cross_entropy_with_logits, 这样做的好处是labels可以不用手动做one_hot省了一些麻烦。这里使用了sgd优化器，学习率为可以根据需要设定。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_entropy_loss = tf.reduce_mean(\n",
    "    tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
    "\n",
    "optimizer = tf.train.GradientDescentOptimizer(\n",
    "    learning_rate=learning_rate).minimize(cross_entropy_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "需要注意的是，上面的网络，最后输出的是未经softmax的原始logits，而不是概率分布， 要想看到概率分布，还需要做一下softmax。\n",
    "\n",
    "将输出的结果与正确结果进行对比，即可得到我们的网络输出结果的准确率。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = tf.nn.softmax(logits)\n",
    "correct_pred = tf.equal(tf.argmax(pred, 1), y)\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "saver用于保存或恢复训练的模型。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "trainig_step = 9000\n",
    "\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上定义的所有操作，均为计算图，也就是仅仅是定义了网络的结构，实际需要运行的话，还需要创建一个session，并将数据填入网络中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 100 training steps, the loss is 0.550061, the validation accuracy is 0.843\n",
      "after 200 training steps, the loss is 0.121427, the validation accuracy is 0.9062\n",
      "after 300 training steps, the loss is 0.121279, the validation accuracy is 0.92\n",
      "after 400 training steps, the loss is 0.472925, the validation accuracy is 0.9076\n",
      "after 500 training steps, the loss is 0.168497, the validation accuracy is 0.9202\n",
      "after 600 training steps, the loss is 0.103491, the validation accuracy is 0.9404\n",
      "after 700 training steps, the loss is 0.28788, the validation accuracy is 0.9428\n",
      "after 800 training steps, the loss is 0.629205, the validation accuracy is 0.9378\n",
      "after 900 training steps, the loss is 0.140389, the validation accuracy is 0.9464\n",
      "after 1000 training steps, the loss is 0.0877731, the validation accuracy is 0.9502\n",
      "after 1100 training steps, the loss is 0.157195, the validation accuracy is 0.9502\n",
      "after 1200 training steps, the loss is 0.0756863, the validation accuracy is 0.9586\n",
      "after 1300 training steps, the loss is 0.0911841, the validation accuracy is 0.9636\n",
      "after 1400 training steps, the loss is 0.0458887, the validation accuracy is 0.939\n",
      "after 1500 training steps, the loss is 0.300299, the validation accuracy is 0.9548\n",
      "after 1600 training steps, the loss is 0.139366, the validation accuracy is 0.961\n",
      "after 1700 training steps, the loss is 0.372737, the validation accuracy is 0.9606\n",
      "after 1800 training steps, the loss is 0.0645524, the validation accuracy is 0.9608\n",
      "after 1900 training steps, the loss is 0.231772, the validation accuracy is 0.9622\n",
      "after 2000 training steps, the loss is 0.153128, the validation accuracy is 0.9642\n",
      "after 2100 training steps, the loss is 0.317067, the validation accuracy is 0.965\n",
      "after 2200 training steps, the loss is 0.149903, the validation accuracy is 0.9488\n",
      "after 2300 training steps, the loss is 0.0239101, the validation accuracy is 0.9626\n",
      "after 2400 training steps, the loss is 0.0742749, the validation accuracy is 0.9674\n",
      "after 2500 training steps, the loss is 0.125433, the validation accuracy is 0.9646\n",
      "after 2600 training steps, the loss is 0.226981, the validation accuracy is 0.9696\n",
      "after 2700 training steps, the loss is 0.0530174, the validation accuracy is 0.9702\n",
      "after 2800 training steps, the loss is 0.0613541, the validation accuracy is 0.9674\n",
      "after 2900 training steps, the loss is 0.0422907, the validation accuracy is 0.9644\n",
      "after 3000 training steps, the loss is 0.0238103, the validation accuracy is 0.9672\n",
      "after 3100 training steps, the loss is 0.0063587, the validation accuracy is 0.9682\n",
      "after 3200 training steps, the loss is 0.15588, the validation accuracy is 0.9638\n",
      "after 3300 training steps, the loss is 0.128339, the validation accuracy is 0.9644\n",
      "after 3400 training steps, the loss is 0.0935526, the validation accuracy is 0.9738\n",
      "after 3500 training steps, the loss is 0.0559626, the validation accuracy is 0.9684\n",
      "after 3600 training steps, the loss is 0.0467844, the validation accuracy is 0.9662\n",
      "after 3700 training steps, the loss is 0.101386, the validation accuracy is 0.97\n",
      "after 3800 training steps, the loss is 0.218824, the validation accuracy is 0.9662\n",
      "after 3900 training steps, the loss is 0.107203, the validation accuracy is 0.973\n",
      "after 4000 training steps, the loss is 0.0171832, the validation accuracy is 0.9722\n",
      "after 4100 training steps, the loss is 0.057239, the validation accuracy is 0.9742\n",
      "after 4200 training steps, the loss is 0.00994665, the validation accuracy is 0.9732\n",
      "after 4300 training steps, the loss is 0.347605, the validation accuracy is 0.9652\n",
      "after 4400 training steps, the loss is 0.00442543, the validation accuracy is 0.9708\n",
      "after 4500 training steps, the loss is 0.0422789, the validation accuracy is 0.9712\n",
      "after 4600 training steps, the loss is 0.035731, the validation accuracy is 0.9706\n",
      "after 4700 training steps, the loss is 0.0173053, the validation accuracy is 0.9734\n",
      "after 4800 training steps, the loss is 0.015085, the validation accuracy is 0.9704\n",
      "after 4900 training steps, the loss is 0.168406, the validation accuracy is 0.9692\n",
      "after 5000 training steps, the loss is 0.0743381, the validation accuracy is 0.973\n",
      "after 5100 training steps, the loss is 0.0299541, the validation accuracy is 0.9748\n",
      "after 5200 training steps, the loss is 0.18846, the validation accuracy is 0.975\n",
      "after 5300 training steps, the loss is 0.0729508, the validation accuracy is 0.9714\n",
      "after 5400 training steps, the loss is 0.0287412, the validation accuracy is 0.972\n",
      "after 5500 training steps, the loss is 0.0180088, the validation accuracy is 0.9768\n",
      "after 5600 training steps, the loss is 0.0584776, the validation accuracy is 0.9708\n",
      "after 5700 training steps, the loss is 0.0226864, the validation accuracy is 0.975\n",
      "after 5800 training steps, the loss is 0.0989082, the validation accuracy is 0.9752\n",
      "after 5900 training steps, the loss is 0.0826117, the validation accuracy is 0.9724\n",
      "after 6000 training steps, the loss is 0.0490005, the validation accuracy is 0.9748\n",
      "after 6100 training steps, the loss is 0.0816818, the validation accuracy is 0.9696\n",
      "after 6200 training steps, the loss is 0.080694, the validation accuracy is 0.976\n",
      "after 6300 training steps, the loss is 0.0665639, the validation accuracy is 0.9734\n",
      "after 6400 training steps, the loss is 0.169993, the validation accuracy is 0.9664\n",
      "after 6500 training steps, the loss is 0.00553127, the validation accuracy is 0.9694\n",
      "after 6600 training steps, the loss is 0.0277194, the validation accuracy is 0.975\n",
      "after 6700 training steps, the loss is 0.0102518, the validation accuracy is 0.975\n",
      "after 6800 training steps, the loss is 0.098071, the validation accuracy is 0.9734\n",
      "after 6900 training steps, the loss is 0.0101806, the validation accuracy is 0.9724\n",
      "after 7000 training steps, the loss is 0.0132218, the validation accuracy is 0.9756\n",
      "after 7100 training steps, the loss is 0.103574, the validation accuracy is 0.9734\n",
      "after 7200 training steps, the loss is 0.2212, the validation accuracy is 0.9722\n",
      "after 7300 training steps, the loss is 0.0163514, the validation accuracy is 0.9748\n",
      "after 7400 training steps, the loss is 0.0224957, the validation accuracy is 0.9778\n",
      "after 7500 training steps, the loss is 0.0200771, the validation accuracy is 0.9784\n",
      "after 7600 training steps, the loss is 0.10232, the validation accuracy is 0.9768\n",
      "after 7700 training steps, the loss is 0.00908802, the validation accuracy is 0.9766\n",
      "after 7800 training steps, the loss is 0.0337132, the validation accuracy is 0.9758\n",
      "after 7900 training steps, the loss is 0.0122367, the validation accuracy is 0.9782\n",
      "after 8000 training steps, the loss is 0.224692, the validation accuracy is 0.9692\n",
      "after 8100 training steps, the loss is 0.00857973, the validation accuracy is 0.9766\n",
      "after 8200 training steps, the loss is 0.116701, the validation accuracy is 0.972\n",
      "after 8300 training steps, the loss is 0.0124196, the validation accuracy is 0.9742\n",
      "after 8400 training steps, the loss is 0.0443748, the validation accuracy is 0.975\n",
      "after 8500 training steps, the loss is 0.00675148, the validation accuracy is 0.9762\n",
      "after 8600 training steps, the loss is 0.206189, the validation accuracy is 0.9762\n",
      "after 8700 training steps, the loss is 0.00269663, the validation accuracy is 0.9774\n",
      "after 8800 training steps, the loss is 0.00216885, the validation accuracy is 0.977\n",
      "after 8900 training steps, the loss is 0.0443266, the validation accuracy is 0.976\n",
      "the training is finish!\n",
      "the test accuarcy is: 0.9749\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "\n",
    "    #定义验证集与测试集\n",
    "    validate_data = {\n",
    "        x: mnist.validation.images,\n",
    "        y: mnist.validation.labels,\n",
    "    }\n",
    "    test_data = {x: mnist.test.images, y: mnist.test.labels}\n",
    "\n",
    "    for i in range(trainig_step):\n",
    "        xs, ys = mnist.train.next_batch(batch_size)\n",
    "        _, loss = sess.run(\n",
    "            [optimizer, cross_entropy_loss],\n",
    "            feed_dict={\n",
    "                x: xs,\n",
    "                y: ys,\n",
    "                learning_rate: 0.3\n",
    "            })\n",
    "\n",
    "        #每100次训练打印一次损失值与验证准确率\n",
    "        if i > 0 and i % 100 == 0:\n",
    "            validate_accuracy = sess.run(accuracy, feed_dict=validate_data)\n",
    "            print(\n",
    "                \"after %d training steps, the loss is %g, the validation accuracy is %g\"\n",
    "                % (i, loss, validate_accuracy))\n",
    "            saver.save(sess, './model.ckpt', global_step=i)\n",
    "\n",
    "    print(\"the training is finish!\")\n",
    "    #最终的测试准确率\n",
    "    acc = sess.run(accuracy, feed_dict=test_data)\n",
    "    print(\"the test accuarcy is:\", acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "下面，用我们训练的模型做一个测试。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model.ckpt-8900\n",
      "1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5bac47e80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    ckpt = tf.train.get_checkpoint_state('./')\n",
    "    if ckpt and ckpt.model_checkpoint_path:\n",
    "        saver.restore(sess, ckpt.model_checkpoint_path)\n",
    "        final_pred, acc = sess.run(\n",
    "            [pred, accuracy],\n",
    "            feed_dict={\n",
    "                x: mnist.test.images[:16],\n",
    "                y: mnist.test.labels[:16]\n",
    "            })\n",
    "        orders = np.argsort(final_pred)\n",
    "        plt.figure(figsize=(8, 8))\n",
    "        print(acc)\n",
    "        for idx in range(16):\n",
    "            order = orders[idx, :][-1]\n",
    "            prob = final_pred[idx, :][order]\n",
    "            plt.subplot(4, 4, idx + 1)\n",
    "            plt.axis('off')\n",
    "            plt.title('{}: [{}]-[{:.1f}%]'.format(mnist.test.labels[idx],\n",
    "                                                  order, prob * 100))\n",
    "            plt.imshow(mnist.test.images[idx].reshape((28, 28)))\n",
    "\n",
    "    else:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以作业提供的参数运行出来的结果，只看上面几个数字还是很不错的，但是总体准确率不太理想。\n",
    "1）、对模型结构的理解：我们这里定义了一个双层神经元网络，首先定义模型的输入（这里我们直接采用直接使用上面的数据作为输入），后定义模型的权重W1和W2（高斯分布的随机分布来初始化权重矩阵）和偏置bias1和bias2，有了这些参数就可以计算为经激活的logits，这里是计算图所以是没有具体参数的，基于这些条件还需要给模型添加lable，这里为了高效运行将图像和lable这两种，多个数据会被组织成一个batch送入网络，还定义了交叉熵的损失以及优化器和accuracy等变量评估模型效果。\n",
    "2）、模型的训练过程：首先我们定义算法公式，然后定义loss（交叉熵损失等），选择合适的优化器来让loss最小，对数据进行迭代训练，目的还是让loss达到最小，最后在测试集或者验证集上对准确率进行评估。\n",
    "3）、对计算图的理解：tensorflow的程序一般分为两个阶段,1、计算图的计算；2、在session中执行计算，简单来说计算图就是为了定义网络的结构的，计算图可以通过命令来查看grap.as_graph_def()，运行这句命令可以看出我们的计算图中包含了许多的node（计算节点，类似jason格式），抽象点理解有点类似c语言中声明一个函数，具体调用时候需要赋予实参（session中运算）。\n",
    "4）、这里的效果为什么差：首先跟我们的模型训练的次数比较少，还没有训练处一个足够好的模型，我将1000次提高到9000，效果有明显变化，其次我们还可以通过调整学习率来试验。（最近加班调休时间比较紧张，作业做的比较仓促，但是通过本次作业还是对tensorflow生成神经网络模型有了很多理解，模型训练的过程中调参是个很耗时间的工作，等放假时间充裕还会对作业多投入精力去继续学习）\n",
    "进阶作业：\n",
    "1、评分达到98以上，这里得出1.0的结果感觉可能有些问题，时间关系没来得及细查。\n",
    "2、调整隐层的数量：将两层去掉一层，即用的单层神经网络，得到的正确率在0.93左右，次数为3000次，学习率也有动态分配，感觉单层神经网络不是很理想。\n",
    "3、调节神经元的个数：将神经元由100调整到10，正确率下降，模型训练的效果不好，0.934的正确率，非常不理想。\n",
    "4、在模型添加正则：时间关系没来得及做。\n",
    "5、不同初始化方式同样也没来得及做，放假时间充裕将参数再好好调整，再训练模型试试。"
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